face_landmark_detection.py

#!/usr/bin/python
from __future__ import print_function
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
#   This example program shows how to find frontal human faces in an image and
#   estimate their pose.  The pose takes the form of 68 landmarks.  These are
#   points on the face such as the corners of the mouth, along the eyebrows, on
#   the eyes, and so forth.
#
#   This face detector is made using the classic Histogram of Oriented
#   Gradients (HOG) feature combined with a linear classifier, an image pyramid,
#   and sliding window detection scheme.  The pose estimator was created by
#   using dlib's implementation of the paper:
#      One Millisecond Face Alignment with an Ensemble of Regression Trees by
#      Vahid Kazemi and Josephine Sullivan, CVPR 2014
#   and was trained on the iBUG 300-W face landmark dataset.
#
#   Also, note that you can train your own models using dlib's machine learning
#   tools. See train_shape_predictor.py to see an example.
#
#   You can get the shape_predictor_68_face_landmarks.dat file from:
#   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
#
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
#   You can install dlib using the command:
#       pip install dlib
#
#   Alternatively, if you want to compile dlib yourself then go into the dlib
#   root folder and run:
#       python setup.py install
#   or
#       python setup.py install --yes USE_AVX_INSTRUCTIONS
#   if you have a CPU that supports AVX instructions, since this makes some
#   things run faster.  
#
#   Compiling dlib should work on any operating system so long as you have
#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
#   running the command:
#       sudo apt-get install libboost-python-dev cmake
#
#   Also note that this example requires scikit-image which can be installed
#   via the command:
#       pip install scikit-image
#   Or downloaded from http://scikit-image.org/download.html. 

import sys
import os
import dlib
import glob
from skimage import io
from multiprocessing.dummy import Pool as ThreadPool 

if len(sys.argv) != 3:
    print(
        "Give the path to the trained shape predictor model as the first "
        "argument and then the directory containing the facial images.\n"
        "For example, if you are in the python_examples folder then "
        "execute this program by running:\n"
        "    ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces *.png\n"
        "You can download a trained facial shape predictor from:\n"
        "    http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
    exit()

predictor_path = './gen/aux/shape_predictor_68_face_landmarks.dat'
faces_folder_path = sys.argv[1]
im_extension = sys.argv[2]

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

def cropImage(path):
    img = io.imread(path)

    # Ask the detector to find the bounding boxes of each face. The 1 in the
    # second argument indicates that we should upsample the image 1 time. This
    # will make everything bigger and allow us to detect more faces.
    dets = detector(img, 1)
    
    # Check for faces (raised IndexError if nothing found)
    if not dets:
        return

    # Get the landmarks/parts for the face in box d.
    shape = predictor(img, dets[0])
    # Print points
    f_parts = path.split('/')
    fname = f_parts[-1]
    fdir = f_parts[-2]
    fout = 'Fast_Marks_' + fname + '.csv'
    
    fout = './gen/aux/temp/csv/' + fdir + '/' + fout
    if os.path.isdir('./gen/aux/temp/csv/' + fdir) is False:
        os.mkdir('./gen/aux/temp/csv/' + fdir)
    fout_handle = open( fout, mode='w')
    for p in range(shape.num_parts):
        fout_handle.write( str(shape.part(p).x) + ',' + str(shape.part(p).y) + '\n' )
    fout_handle.close()
    print(fout + 'id: ' + fdir)
        

if os.path.isdir('./gen/aux/temp/csv') is False:
    os.mkdir('./gen/aux/temp/csv')

files = glob.glob(os.path.join(faces_folder_path, im_extension))

pool = ThreadPool(8)
res = pool.map(cropImage, files)

#os.system('matlab -nosplash -nodesktop -nodisplay -r "generate ' + faces_folder_path + ' ' + im_extension + '"')